TADA Module 1: Training for Intermediate/Advanced R Users
TADA Team
2024-05-31
Source:vignettes/TADAModule1_AdvancedTraining.Rmd
TADAModule1_AdvancedTraining.RmdWelcome!
Thank you for your interest in Tools for Automated Data Analysis (TADA). TADA is an open-source tool set built in the R programming language and available for anyone to download and edit to their specific needs. This RMarkdown document walks users through how to download the TADA R package from GitHub, access and parameterize several important functions with a sample dataset, and create basic visualizations. The workflow is similar to a funnel: at each decision point, data that fail QC checks are removed from the core dataset and placed in a separate dataframe, while data that pass are carried into the next step. At the end of the QC checks, the user should be confident that their data are properly documented and applicable to the analysis at hand.
Note: TADA is still under development. New functionality is added weekly, and sometimes we need to make bug fixes in response to tester and user feedback. We appreciate your feedback, patience, and interest in these helpful tools.
Customize or contribute
TADA is housed in a repository on GitHub. Users desiring to review the base code and customize the package for their own purposes may:
Clone the repository using Git
Open the repository using GitHub Desktop, or
Download a zip file of the repository to their desktop.
Interested in contributing to the TADA package? The TADA team highly encourages input and development from users. Check out the Contributing page on the TADA GitHub site for guidance on collaboration conventions.
Install and setup
Users can install the TADA package from GitHub into their R library
using the remotes package. Copy and paste the code below
into your R or RStudio console to download and install.
install.packages("remotes",
repos = "http://cran.us.r-project.org"
)
library(remotes)TADA package relies on other packages, therefore you may be prompted in the console to update dependency packages that have more recent versions available. If you see this prompt, it is recommended to update all of them (enter 1 into the console).
remotes::install_github("USEPA/TADA",
ref = "develop",
dependencies = TRUE
)It’s that easy! The most stable branch for TADA right now is the
develop branch. Contributors generally create their own branches based
on develop, make some improvements, and then submit a pull request to be
reviewed by the TADA Team. Once approved, updates are then merged into
the develop branch. However, you are welcome to download any branch
you’d like using the ref input in
install_github (see code chunk above). This functionality
is mainly only useful to TADA package developers/contributors.
The following code block ensures the additional packages needed to
run the code in this RMarkdown document are loaded. However, users may
also use the package name:: package function notation to
avoid the list of library() calls.
list.of.packages <- c("tidyverse")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[, "Package"])]
if (length(new.packages)) install.packages(new.packages)
library(tidyverse)Help pages
All TADA R package functions have their own individual help pages,
listed on the Function
reference page on the GitHub site. Users can also access the help
page for a given function in R or RStudio using the following format
(example below): ?TADA::[name of TADA function]
?TADA::TADA_DataRetrievalUpload data
Now let’s start using the TADA R package functions. The first step is to bring a dataset into the R environment. TADA is designed to work with Water Quality Portal (WQP) data. This means that all of its functions will look for WQP column names and create new TADA-specific columns based on these elements. Users may upload their own custom dataset into R for use with TADA by ensuring their column names and data formats (e.g. numeric, character) align with WQP profiles.
If you are interested in reviewing the column headers and formats
required to run TADA, use the function below, which saves an example
spreadsheet to the user’s working directory. You can also take a look at
an example dataset, like TADA::Data_Nutrients_UT to get an
idea of the data structure and format.
getwd() # find your working directory## [1] "/home/runner/work/TADA/TADA/vignettes"
template <- TADA::TADA_GetTemplate() # download template to working directory
# uncomment below to review example dataset
# Data_Nutrients_UT <- TADA::Data_Nutrients_UTTADA_DataRetrieval is built upon USGS’s
readWQPdata function within the dataRetrieval package,
which uses web service calls to bring WQP data into the R environment.
Additionally, TADA_DataRetrieval performs some basic
quality control checks via TADA_AutoClean on the data using
new TADA-specific columns to preserve the original dataset:
Converts key character columns to ALL CAPS for easier harmonization and validation.
Removes complete duplicate results.
Identifies different classes of result values (numeric, text, percentage, comma-separated numeric, greater than/less than, numbers preceded by a tilde, etc.) and converts values to numeric where feasible.
Unifies result and depth units to common units to improve ease of data harmonization. See
?TADA_ConvertResultUnitsand?TADA_ConvertDepthUnitsfor more information on these processes. These functions can also be run separately if the user wishes to convert result or depth values to different units.
Let’s give it a try. Setting applyautoclean to TRUE in
TADA_DataRetrieval means that the basic quality control
steps described above are run. TADA_DataRetrieval follows
similar parameterization to readWQPdata, but check out the
help page or enter
?TADA::TADA_DataRetrieval into the console for more
information about input parameters and to see several examples.
# download example data
# dataset_0 <- TADA_DataRetrieval(
# organization = c("REDLAKE_WQX", "SFNOES_WQX", "PUEBLO_POJOAQUE", "FONDULAC_WQX", "PUEBLOOFTESUQUE", "CNENVSER"),
# startDate = "2018-01-01",
# endDate = "2019-01-01")
# For brevity, we'll skip pinging the WQP and instead load the example dataset:
dataset_0 <- TADA::Data_6Tribes_5y
# Let's take a look at all of the TADA-created columns:
names(dataset_0)[grepl("TADA.", names(dataset_0))]## [1] "TADA.ActivityMediaName"
## [2] "TADA.ResultSampleFractionText"
## [3] "TADA.CharacteristicName"
## [4] "TADA.MethodSpeciationName"
## [5] "TADA.ComparableDataIdentifier"
## [6] "TADA.ResultMeasureValue"
## [7] "TADA.ResultMeasureValueDataTypes.Flag"
## [8] "TADA.ResultMeasure.MeasureUnitCode"
## [9] "TADA.WQXResultUnitConversion"
## [10] "TADA.DetectionQuantitationLimitMeasure.MeasureValue"
## [11] "TADA.DetectionQuantitationLimitMeasure.MeasureValueDataTypes.Flag"
## [12] "TADA.DetectionQuantitationLimitMeasure.MeasureUnitCode"
## [13] "TADA.ResultDepthHeightMeasure.MeasureValue"
## [14] "TADA.ResultDepthHeightMeasure.MeasureValueDataTypes.Flag"
## [15] "TADA.ResultDepthHeightMeasure.MeasureUnitCode"
## [16] "TADA.ActivityDepthHeightMeasure.MeasureValue"
## [17] "TADA.ActivityDepthHeightMeasure.MeasureValueDataTypes.Flag"
## [18] "TADA.ActivityDepthHeightMeasure.MeasureUnitCode"
## [19] "TADA.ActivityTopDepthHeightMeasure.MeasureValue"
## [20] "TADA.ActivityTopDepthHeightMeasure.MeasureValueDataTypes.Flag"
## [21] "TADA.ActivityTopDepthHeightMeasure.MeasureUnitCode"
## [22] "TADA.ActivityBottomDepthHeightMeasure.MeasureValue"
## [23] "TADA.ActivityBottomDepthHeightMeasure.MeasureValueDataTypes.Flag"
## [24] "TADA.ActivityBottomDepthHeightMeasure.MeasureUnitCode"
## [25] "TADA.LatitudeMeasure"
## [26] "TADA.LongitudeMeasure"Currently, the TADA_DataRetrieval function combines
three WQP data profiles: Sample Results (Physical/Chemical), Site data,
and Project data. This ensures that all important quality control
columns are included in the dataset.
Note: USGS and EPA are working together to create
WQP 3.0 data profiles. Once released, one data profile will contain the
columns critical to TADA, removing the need to combine profiles in this
first step. TADA package users likely will not notice a difference in
their usage of the TADA_DataRetrieval function, but it will
simplify the steps needed to upload a custom or WQP GUI-downloaded
dataset into the R package.
Initial data review
Now that we’ve pulled the data into the R session, let’s take a look
at it. Note that any column names with the “TADA.” prefix were generated
from the TADA_DataRetrieval function.
First, always good to take a look at the data frame dimensions.
Question 1: What are the dimensions of your dataset?
dim(dataset_0) # returns x and of x (as the numbers of rows and columns respectively)## [1] 153054 149Before we start filtering and flagging our data, let’s create a
function (dimCheck) that performs dimension checks between
the results that pass each filter or QC flag check (and are retained)
and those that do not (and are removed). These dimension checks ensure
that the total number of rows in the original input dataset
(all_result_num) equal the the total number of rows added
up between the passing (pass_data) and removed
(fail_data) data frames.
# defining a dimension check function that compares removed and retained data dimensions against the initial data input
dimCheck <- function(all_result_num, pass_data, fail_data, checkName) {
# check result numbers after split
final_result_num <- dim(pass_data)[1] + dim(fail_data)[1]
# always good to do a dimension check
if (!all_result_num == final_result_num) {
print(paste0("Help! Results do not add up between dataset and removed after ", checkName, " check."))
} else {
print(paste0("Good to go. Zero results created or destroyed in ", checkName, " check."))
}
}
# let's first get the total number of rows in the dataset.
all_result_num <- dim(dataset_0)[1]Next, we can use the TADA TADA_FieldCounts() function to
see how many unique values are contained within each column of the
dataset. The function can either return all column counts, most, or just
the key columns. We’ll try the input with display = "key"
and display = "all". Enter
?TADA::TADA_FieldCounts() into the console for more
information on this function.
Question 2: Which column should have a unique value in every row and why?
key_counts <- TADA::TADA_FieldCounts(dataset_0, display = "key")
key_counts## Fields Count
## 1 ActivityCommentText 898
## 2 TADA.CharacteristicName 139
## 3 ProjectName 32
## 4 LaboratoryName 9
## 5 ActivityTypeCode 7
## 6 OrganizationFormalName 6
## 7 DetectionQuantitationLimitTypeName 6
## 8 MonitoringLocationTypeName 5
## 9 StateCode 4
## 10 TADA.ActivityMediaName 3
## 11 ActivityMediaSubdivisionName 2
all_counts <- TADA::TADA_FieldCounts(dataset_0, display = "all")
all_counts## Fields Count
## 1 ResultIdentifier 153054
## 2 TADA.ResultMeasureValue 41055
## 3 ResultMeasureValue 37271
## 4 ActivityIdentifier 21089
## 5 ActivityStartDateTime 13376
## 6 ResultDetectionQuantitationLimitUrl 11924
## 7 LastUpdated 6989
## 8 TADA.ResultDepthHeightMeasure.MeasureValue 3938
## 9 ResultDepthHeightMeasure.MeasureValue 3848
## 10 ActivityStartTime.Time 3757
## 11 ActivityEndDateTime 1026
## 12 ActivityEndTime.Time 1001
## 13 ActivityCommentText 898
## 14 ActivityStartDate 854
## 15 AnalysisStartDate 545
## 16 DetectionQuantitationLimitMeasure.MeasureValue 382
## 17 TADA.DetectionQuantitationLimitMeasure.MeasureValue 381
## 18 ResultCommentText 381
## 19 SubjectTaxonomicName 285
## 20 ActivityLocation.LongitudeMeasure 279
## 21 ActivityLocation.LatitudeMeasure 276
## 22 MonitoringLocationIdentifier 235
## 23 MonitoringLocationName 230
## 24 LongitudeMeasure 225
## 25 TADA.LongitudeMeasure 225
## 26 LatitudeMeasure 222
## 27 TADA.LatitudeMeasure 221
## 28 TADA.ComparableDataIdentifier 208
## 29 ActivityDepthHeightMeasure.MeasureValue 166
## 30 TADA.ActivityDepthHeightMeasure.MeasureValue 163
## 31 CharacteristicName 140
## 32 TADA.CharacteristicName 139
## 33 DataQuality.UpperConfidenceLimitValue 95
## 34 ResultAnalyticalMethod.MethodIdentifier 70
## 35 ResultAnalyticalMethod.MethodName 68
## 36 ActivityEndDate 68
## 37 DataQuality.LowerConfidenceLimitValue 64
## 38 ActivityBottomDepthHeightMeasure.MeasureValue 55
## 39 TADA.ActivityBottomDepthHeightMeasure.MeasureValue 54
## 40 ResultAnalyticalMethod.MethodDescriptionText 40
## 41 ResultMeasure.MeasureUnitCode 35
## 42 ProjectName 32
## 43 ProjectIdentifier 32
## 44 TADA.ResultMeasure.MeasureUnitCode 22
## 45 ProjectDescriptionText 19
## 46 SampleCollectionMethod.MethodName 15
## 47 CountyCode 15
## 48 HUCEightDigitCode 15
## 49 SampleCollectionMethod.MethodIdentifier 14
## 50 MonitoringLocationDescriptionText 14
## 51 SampleCollectionMethod.MethodDescriptionText 13
## 52 ResultAnalyticalMethod.MethodIdentifierContext 12
## 53 DetectionQuantitationLimitMeasure.MeasureUnitCode 11
## 54 SampleCollectionEquipmentName 11
## 55 MethodSpeciationName 10
## 56 TADA.MethodSpeciationName 10
## 57 TADA.ResultMeasureValueDataTypes.Flag 9
## 58 MeasureQualifierCode 9
## 59 LaboratoryName 9
## 60 ResultSampleFractionText 8
## 61 TADA.ResultSampleFractionText 8
## 62 TADA.DetectionQuantitationLimitMeasure.MeasureUnitCode 8
## 63 ActivityTypeCode 7
## 64 ResultDetectionConditionText 7
## 65 SampleCollectionMethod.MethodIdentifierContext 7
## 66 DetectionQuantitationLimitTypeName 6
## 67 OrganizationIdentifier 6
## 68 OrganizationFormalName 6
## 69 MonitoringLocationTypeName 5
## 70 ActivityStartTime.TimeZoneCode 4
## 71 TADA.ActivityDepthHeightMeasure.MeasureValueDataTypes.Flag 4
## 72 QAPPApprovalAgencyName 4
## 73 StateCode 4
## 74 timeZoneStart 4
## 75 ActivityMediaName 3
## 76 TADA.ActivityMediaName 3
## 77 ResultValueTypeName 3
## 78 TADA.DetectionQuantitationLimitMeasure.MeasureValueDataTypes.Flag 3
## 79 ResultDepthHeightMeasure.MeasureUnitCode 3
## 80 ActivityRelativeDepthName 3
## 81 TADA.ActivityBottomDepthHeightMeasure.MeasureValueDataTypes.Flag 3
## 82 StatisticalBaseCode 3
## 83 QAPPApprovedIndicator 3
## 84 ActivityEndTime.TimeZoneCode 3
## 85 timeZoneEnd 3
## 86 ActivityMediaSubdivisionName 2
## 87 TADA.WQXResultUnitConversion 2
## 88 TADA.ResultDepthHeightMeasure.MeasureValueDataTypes.Flag 2
## 89 TADA.ResultDepthHeightMeasure.MeasureUnitCode 2
## 90 ActivityDepthHeightMeasure.MeasureUnitCode 2
## 91 TADA.ActivityDepthHeightMeasure.MeasureUnitCode 2
## 92 ActivityTopDepthHeightMeasure.MeasureValue 2
## 93 TADA.ActivityTopDepthHeightMeasure.MeasureValue 2
## 94 TADA.ActivityTopDepthHeightMeasure.MeasureValueDataTypes.Flag 2
## 95 ActivityTopDepthHeightMeasure.MeasureUnitCode 2
## 96 TADA.ActivityTopDepthHeightMeasure.MeasureUnitCode 2
## 97 ActivityBottomDepthHeightMeasure.MeasureUnitCode 2
## 98 TADA.ActivityBottomDepthHeightMeasure.MeasureUnitCode 2
## 99 CountryCode 2
## 100 HorizontalCoordinateReferenceSystemDatumName 2
## 101 ActivityConductingOrganizationText 2
## 102 ResultStatusIdentifier 2
## 103 SourceMapScaleNumeric 2
## 104 HorizontalCollectionMethodName 2
## 105 ProviderName 1Question 3: How many unique ‘TADA.ActivityMediaName’ values exist in your dataset? Are there any media types that are not water?
TADA is currently designed to accommodate water data from the WQP. Let’s ensure that we remove all non-water data first.
# remove data with media type that is not water
removed <- dataset_0 %>%
dplyr::filter(!TADA.ActivityMediaName %in% c("WATER")) %>%
dplyr::mutate(TADA.RemovalReason = "Activity media is not water.")
# what other media types exist in dataset?
unique(removed$TADA.ActivityMediaName)## [1] "BIOLOGICAL" "AIR"
# clean dataset contains only water
dataset <- dataset_0 %>% dplyr::filter(TADA.ActivityMediaName %in% c("WATER"))
dimCheck(all_result_num, dataset, removed, checkName = "Activity Media")## [1] "Good to go. Zero results created or destroyed in Activity Media check."Two additional helper functions one can use at any step in the
process are TADA_FieldValuesTable() and
TADA_FieldValuesPie(). These functions create a summary
table and pie chart (respectively) of all the unique values in a given
column. Let’s give it a try on OrganizationFormalName, which is a WQP
column naming the organization that supplied the result.
TADA::TADA_FieldValuesPie(dataset, field = "OrganizationFormalName")
org_counts <- TADA::TADA_FieldValuesTable(dataset, field = "OrganizationFormalName")
org_counts## Value Count
## 1 Red Lake DNR 104358
## 2 Fond du Lac Band of Chippewa (MN) 19764
## 3 Sac and Fox Nation (Tribal) 9943
## 4 Pueblo Of Tesuque (Tribal) 6798
## 5 Chickasaw Nation Environmental Service (Tribal) 6071
## 6 Pueblo of Pojoaque 1441Question 4: When might a user choose to view a column’s unique values as a table rather than in a pie chart?
We can take a quick look at some of the TADA-created columns that
review result value types. Because TADA is intended to work with numeric
data, at this point, it would be good to remove those result values that
are NA without any detection limit info, or contain text or special
characters that cannot be converted to numeric. Note that TADA will fill
in missing values with detection limit values and units with the
TADA_IDCensoredData if the ResultDetectionConditionText and
DetectionQuantitationLimitType fields are populated. Use
?TADA_ConvertSpecialChars for more details on result value
types and handling and ?TADA_IDCensoredData for details on
censored data preparation.
First, we can run TADA_IDCensoredData to fill in as many
NA/missing values as possible. We can use
TADA_FieldValuesPie to view the censored data flags
identified in the data set and their relative frequency.
TADA_IDCensoredData sorts result values into detection
limit categories (e.g. non-detect, over-detect) based on populated
values in the ResultDetectionConditionText and
DetectionQuantitationLimitTypeName columns.
You can find the reference tables used to make these decisions in
TADA_GetDetCondRef() and TADA_GetDetLimitRef()
functions. In some cases, results are missing detection limit/condition
info, or there is a conflict in the detection limit and condition. The
user may want to remove problematic detection limit data before
proceeding. We can also filter for the “problem” data by
TADA.CensoredData.Flag and review the unique reasons for data
removal.
dataset <- TADA::TADA_IDCensoredData(dataset)## [1] "TADA_IDCensoredData: There are 105 results in your dataset that are missing ResultDetectionConditionText. TADA requires BOTH ResultDetectionConditionText and DetectionQuantitationLimitTypeName fields to be populated in order to categorize censored data."
## [1] "TADA_IDCensoredData: DetectionQuantitationLimitTypeName column dataset contains value(s) NA which is/are not represented in the DetectionQuantitationLimitTypeName WQX domain table. These data records are placed under the TADA.CensoredData.Flag: Censored but not Categorized, and will not be used in censored data handling methods. Please contact TADA administrators to resolve."
TADA::TADA_FieldValuesPie(dataset, field = "TADA.CensoredData.Flag")
problem_censored <- dataset %>%
dplyr::filter(!TADA.CensoredData.Flag %in% c("Non-Detect", "Over-Detect", "Other", "Uncensored")) %>%
dplyr::mutate(TADA.RemovalReason = "Detection limit information contains errors or missing information.")
# Let's take a look at the problematic data that we filtered out (if any)
check <- unique(problem_censored[, c("TADA.CharacteristicName", "ResultDetectionConditionText", "DetectionQuantitationLimitTypeName", "TADA.CensoredData.Flag")])
check## TADA.CharacteristicName ResultDetectionConditionText
## 1 PHEOPHYTIN A <NA>
## 2 ORTHOPHOSPHATE <NA>
## 4 NITRATE <NA>
## 5 NITRITE <NA>
## 13 ALKALINITY, TOTAL <NA>
## 17 CHROMIUM <NA>
## 18 COPPER <NA>
## 94 SULFATE <NA>
## DetectionQuantitationLimitTypeName
## 1 <NA>
## 2 Method Detection Level
## 4 Method Detection Level
## 5 Method Detection Level
## 13 Method Detection Level
## 17 Method Detection Level
## 18 Method Detection Level
## 94 Method Detection Level
## TADA.CensoredData.Flag
## 1 Detection condition is missing and required for censored data ID.
## 2 Detection condition is missing and required for censored data ID.
## 4 Detection condition is missing and required for censored data ID.
## 5 Detection condition is missing and required for censored data ID.
## 13 Detection condition is missing and required for censored data ID.
## 17 Detection condition is missing and required for censored data ID.
## 18 Detection condition is missing and required for censored data ID.
## 94 Detection condition is missing and required for censored data ID.
dataset <- dataset %>% dplyr::filter(TADA.CensoredData.Flag %in% c("Non-Detect", "Over-Detect", "Other", "Uncensored"))
# Let's take a look at the removed data
removed <- plyr::rbind.fill(removed, problem_censored)
# dimension check
dimCheck(all_result_num, dataset, removed, checkName = "Censored Data")## [1] "Good to go. Zero results created or destroyed in Censored Data check."Next, we can take a look at the data types present and filter out any non-allowable types.
# take a look at datatypes
flag.datatypes <- TADA::TADA_FieldValuesTable(dataset, field = "TADA.ResultMeasureValueDataTypes.Flag")
# Numeric or numeric-coerced data types
rv_datatypes <- unique(subset(dataset, !is.na(dataset$TADA.ResultMeasureValue))$TADA.ResultMeasureValueDataTypes.Flag)
# Non-numeric data types coerced to NA
na_rv_datatypes <- unique(subset(dataset, is.na(dataset$TADA.ResultMeasureValue))$TADA.ResultMeasureValueDataTypes.Flag)
# these are all of the NOT allowable data types in the dataset.
incompatible_datatype <- dataset %>%
dplyr::filter(!dataset$TADA.ResultMeasureValueDataTypes.Flag %in% c("Numeric", "Less Than", "Greater Than", "Approximate Value", "Percentage", "Comma-Separated Numeric", "Numeric Range - Averaged", "Result Value/Unit Copied from Detection Limit")) %>%
dplyr::mutate(TADA.RemovalReason = "Result value type cannot be converted to numeric or no detection limit values provided.")
# take a look at the difficult data types - do they make sense?
check <- unique(incompatible_datatype[, c("TADA.CharacteristicName", "ResultMeasureValue", "TADA.ResultMeasureValue", "ResultMeasure.MeasureUnitCode", "TADA.ResultMeasure.MeasureUnitCode", "TADA.ResultMeasureValueDataTypes.Flag", "DetectionQuantitationLimitMeasure.MeasureValue", "TADA.DetectionQuantitationLimitMeasure.MeasureValue", "DetectionQuantitationLimitMeasure.MeasureUnitCode", "TADA.DetectionQuantitationLimitMeasure.MeasureUnitCode")])
check## TADA.CharacteristicName
## 1 CLOUD COVER
## 4 WIND VELOCITY
## 5 CLOUD COVER
## 15 CLOUD COVER
## 16 LOONS, VISUAL OBSERVATION
## 22 HEIGHT, GAGE
## 32 WATER APPEARANCE (TEXT)
## 34 WATER APPEARANCE (TEXT)
## 56 WATER APPEARANCE (TEXT)
## 57 WATER APPEARANCE (TEXT)
## 59 WATER APPEARANCE (TEXT)
## 61 WATER APPEARANCE (TEXT)
## 64 WATER APPEARANCE (TEXT)
## 70 WATER APPEARANCE (TEXT)
## 86 WATER APPEARANCE (TEXT)
## 90 WATER APPEARANCE (TEXT)
## 107 WATER APPEARANCE (TEXT)
## 112 WATER APPEARANCE (TEXT)
## 115 WATER APPEARANCE (TEXT)
## 152 WATER APPEARANCE (TEXT)
## 184 WATER APPEARANCE (TEXT)
## 186 WATER APPEARANCE (TEXT)
## 236 WATER APPEARANCE (TEXT)
## 254 WATER APPEARANCE (TEXT)
## 300 WATER APPEARANCE (TEXT)
## 314 WATER APPEARANCE (TEXT)
## 389 WATER APPEARANCE (TEXT)
## 446 WATER APPEARANCE (TEXT)
## 512 WATER APPEARANCE (TEXT)
## 522 WATER APPEARANCE (TEXT)
## 528 WATER APPEARANCE (TEXT)
## 598 WATER APPEARANCE (TEXT)
## 618 WATER APPEARANCE (TEXT)
## 620 WATER APPEARANCE (TEXT)
## 692 WATER APPEARANCE (TEXT)
## 824 WATER APPEARANCE (TEXT)
## 837 WATER APPEARANCE (TEXT)
## 984 WATER APPEARANCE (TEXT)
## 1123 FLOW
## 1124 BAROMETRIC PRESSURE
## 1125 BAROMETRIC PRESSURE
## 1133 WATER APPEARANCE (TEXT)
## 1333 WATER APPEARANCE (TEXT)
## 1523 WATER APPEARANCE (TEXT)
## 1635 TEMPERATURE, WATER
## 1636 DISSOLVED OXYGEN SATURATION
## 1637 CONDUCTIVITY
## 1638 TOTAL DISSOLVED SOLIDS
## 1663 WATER APPEARANCE (TEXT)
## 1939 WATER APPEARANCE (TEXT)
## 2047 WATER APPEARANCE (TEXT)
## 2066 WATER APPEARANCE (TEXT)
## 2268 WATER APPEARANCE (TEXT)
## 2353 LAKE RECREATIONAL SUITABILITY (CHOICE LIST)
## 2393 LAKE RECREATIONAL SUITABILITY (CHOICE LIST)
## 2401 LAKE RECREATIONAL SUITABILITY (CHOICE LIST)
## 2447 WATER APPEARANCE (TEXT)
## 3277 WATER APPEARANCE (TEXT)
## 3283 WATER APPEARANCE (TEXT)
## 3327 LAKE RECREATIONAL SUITABILITY (CHOICE LIST)
## ResultMeasureValue
## 1 foggy
## 4 Calm
## 5 clear_sunny
## 15 raining
## 16 <NA>
## 22 1:78
## 32 Clear; Crystal Clear
## 34 Stain; Crystal Clear
## 56 Sediment; some algae present
## 57 Stain; some algae present
## 59 Sediment
## 61 Stain; Some algae present
## 64 Stain
## 70 Green; some algae present
## 86 Green; Some algae present
## 90 Stained; Crystal Clear
## 107 Green; crystal clear
## 112 Sediment; Some algae present
## 115 Clear water color; crystal clear physical conditions
## 152 Clear; some algae present
## 184 Stain; crystal clear
## 186 Green; definite algae present
## 236 Clear
## 254 Green
## 300 Tea-colored
## 314 Clear; Some algae present
## 389 Stain; Crystal clear
## 446 Cloudy
## 512 Water color clear; physical condition crystal clear
## 522 Water color stained; physical condition crystal clear
## 528 Stain; Definite algae present
## 598 Green; Crystal Clear
## 618 Sediment; Crystal clear
## 620 Sediment; Crystal Clear
## 692 Green; Definite algae present
## 824 Sediment; Definitie algae present
## 837 Green; Crystal clear
## 984 Stained
## 1123 <NA>
## 1124 600.7061 mmHG
## 1125 600.71 mmHG
## 1133 Clear; Crystal clear
## 1333 Sediment; Definite algae present
## 1523 Stain; definite algae present
## 1635 None
## 1636 mg/l
## 1637 mg/l
## 1638 PSS
## 1663 Clear; crystal clear
## 1939 Clear; Definite algae present
## 2047 Sediment; Stage tape-down from bridge
## 2066 Sediment; definite algae present
## 2268 Muddy
## 2353 1.VERY GOOD
## 2393 2.GOOD
## 2401 3.FAIR
## 2447 Green; High algal color
## 3277 Sediment; Clear
## 3283 Green; Clear
## 3327 4.POOR
## TADA.ResultMeasureValue ResultMeasure.MeasureUnitCode
## 1 NA %
## 4 NA mph
## 5 NA %
## 15 NA %
## 16 NA <NA>
## 22 NA ft
## 32 NA <NA>
## 34 NA <NA>
## 56 NA <NA>
## 57 NA <NA>
## 59 NA <NA>
## 61 NA <NA>
## 64 NA <NA>
## 70 NA <NA>
## 86 NA <NA>
## 90 NA <NA>
## 107 NA <NA>
## 112 NA <NA>
## 115 NA <NA>
## 152 NA <NA>
## 184 NA <NA>
## 186 NA <NA>
## 236 NA <NA>
## 254 NA <NA>
## 300 NA <NA>
## 314 NA <NA>
## 389 NA <NA>
## 446 NA <NA>
## 512 NA <NA>
## 522 NA <NA>
## 528 NA <NA>
## 598 NA <NA>
## 618 NA <NA>
## 620 NA <NA>
## 692 NA <NA>
## 824 NA <NA>
## 837 NA <NA>
## 984 NA <NA>
## 1123 NA cfs
## 1124 NA psi
## 1125 NA psi
## 1133 NA <NA>
## 1333 NA <NA>
## 1523 NA <NA>
## 1635 NA deg C
## 1636 NA %
## 1637 NA uS/cm
## 1638 NA mg/L
## 1663 NA <NA>
## 1939 NA <NA>
## 2047 NA <NA>
## 2066 NA <NA>
## 2268 NA <NA>
## 2353 NA <NA>
## 2393 NA <NA>
## 2401 NA <NA>
## 2447 NA <NA>
## 3277 NA <NA>
## 3283 NA <NA>
## 3327 NA <NA>
## TADA.ResultMeasure.MeasureUnitCode TADA.ResultMeasureValueDataTypes.Flag
## 1 % Text
## 4 M/SEC Text
## 5 % Text
## 15 % Text
## 16 <NA> NA - Not Available
## 22 IN Coerced to NA
## 32 <NA> Text
## 34 <NA> Text
## 56 <NA> Text
## 57 <NA> Text
## 59 <NA> Text
## 61 <NA> Text
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## 824 <NA> Text
## 837 <NA> Text
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## 1123 CFS NA - Not Available
## 1124 G/M2 Text
## 1125 G/M2 Text
## 1133 <NA> Text
## 1333 <NA> Text
## 1523 <NA> Text
## 1635 DEG C Text
## 1636 % Text
## 1637 US/CM Text
## 1638 UG/L Text
## 1663 <NA> Text
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## DetectionQuantitationLimitMeasure.MeasureValue
## 1 <NA>
## 4 <NA>
## 5 <NA>
## 15 <NA>
## 16 <NA>
## 22 <NA>
## 32 <NA>
## 34 <NA>
## 56 <NA>
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## 2353 <NA>
## 2393 <NA>
## 2401 <NA>
## 2447 <NA>
## 3277 <NA>
## 3283 <NA>
## 3327 <NA>
## TADA.DetectionQuantitationLimitMeasure.MeasureValue
## 1 NA
## 4 NA
## 5 NA
## 15 NA
## 16 NA
## 22 NA
## 32 NA
## 34 NA
## 56 NA
## 57 NA
## 59 NA
## 61 NA
## 64 NA
## 70 NA
## 86 NA
## 90 NA
## 107 NA
## 112 NA
## 115 NA
## 152 NA
## 184 NA
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## 389 NA
## 446 NA
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## 528 NA
## 598 NA
## 618 NA
## 620 NA
## 692 NA
## 824 NA
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## 984 NA
## 1123 NA
## 1124 NA
## 1125 NA
## 1133 NA
## 1333 NA
## 1523 NA
## 1635 NA
## 1636 NA
## 1637 NA
## 1638 NA
## 1663 NA
## 1939 NA
## 2047 NA
## 2066 NA
## 2268 NA
## 2353 NA
## 2393 NA
## 2401 NA
## 2447 NA
## 3277 NA
## 3283 NA
## 3327 NA
## DetectionQuantitationLimitMeasure.MeasureUnitCode
## 1 <NA>
## 4 <NA>
## 5 <NA>
## 15 <NA>
## 16 <NA>
## 22 <NA>
## 32 <NA>
## 34 <NA>
## 56 <NA>
## 57 <NA>
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## 2353 <NA>
## 2393 <NA>
## 2401 <NA>
## 2447 <NA>
## 3277 <NA>
## 3283 <NA>
## 3327 <NA>
## TADA.DetectionQuantitationLimitMeasure.MeasureUnitCode
## 1 <NA>
## 4 <NA>
## 5 <NA>
## 15 <NA>
## 16 <NA>
## 22 <NA>
## 32 <NA>
## 34 <NA>
## 56 <NA>
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## 2353 <NA>
## 2393 <NA>
## 2401 <NA>
## 2447 <NA>
## 3277 <NA>
## 3283 <NA>
## 3327 <NA>Then we can take a closer look at the removed results and run another dimension check on the data set.
# filter data set to include allowable data types
dataset <- dataset %>% dplyr::filter(dataset$TADA.ResultMeasureValueDataTypes.Flag %in% c("Numeric", "Less Than", "Greater Than", "Approximate Value", "Percentage", "Comma-Separated Numeric", "Numeric Range - Averaged", "Result Value/Unit Copied from Detection Limit"))
# create data frame to includ all removed results
removed <- plyr::rbind.fill(removed, incompatible_datatype)
# dimension check
dimCheck(all_result_num, dataset, removed, checkName = "Result Format")## [1] "Good to go. Zero results created or destroyed in Result Format check."Data flagging
We’ve taken a quick look at the raw dataset and split off some data
that are not compatible with TADA, now let’s run through some quality
control checks. The most important ones to run to ensure your dataset is
ready for subsequent steps are TADA_FlagFraction(),
TADA_FlagSpeciation(), TADA_FlagResultUnit(),
and TADA_FindQCActivities(). With the exception of
TADA_FindQCActivities(), these flagging functions leverage
WQX’s QAQC
Validation Table. TADA_FindQCActivities() uses a
TADA-specific domain table users can review with
TADA_GetActivityTypeRef(). All QAQC tables are frequently
updated in the package to ensure they match the latest version on the
web. You can find guidance for using the WQX QAQC Validation Tables in
this vignette
on the TADA GitHub site.
Bring the QAQC Validation Table into your R session to view or save with the following command:
qaqc_ref <- TADA::TADA_GetWQXCharValRef()
unique(qaqc_ref$Type)## [1] "CharacteristicFraction" "CharacteristicMethod"
## [3] "CharacteristicSpeciation" "CharacteristicUnit"Question 5: What do you think the qaqc_ref$Type
column indicates?
TADA joins this validation table to the data and uses the “Valid” and “Invalid” labels in the Status column to create easily understandable flagging columns for each function. Let’s run these four flagging functions.
dataset_flags <- TADA::TADA_FlagFraction(dataset, clean = FALSE, flaggedonly = FALSE)## [1] "Rows with invalid sample fractions have been flagged but retained. Review these rows before proceeding and/or set clean = TRUE."
dataset_flags <- TADA::TADA_FlagSpeciation(dataset_flags, clean = "none", flaggedonly = FALSE)## [1] "Rows with invalid speciations have been flagged but retained. Review these rows before proceeding and/or set clean = 'invalid_only' or 'both'."
dataset_flags <- TADA::TADA_FlagResultUnit(dataset_flags, clean = "none", flaggedonly = FALSE)## [1] "Rows with invalid result value units have been flagged but retained. Review these rows before proceeding and/or set clean = 'invalid_only' or 'both'."
dataset_flags <- TADA::TADA_FindQCActivities(dataset_flags, clean = FALSE, flaggedonly = FALSE)
dimCheck(all_result_num, dataset_flags, removed, checkName = "Run Flag Functions")## [1] "Good to go. Zero results created or destroyed in Run Flag Functions check."Question 6: Did any warnings or messages appear in the console after running these flagging functions? What do they say?
Now that we’ve run all the key flagging functions, let’s take a look at the results and make some decisions.
TADA::TADA_FieldValuesPie(dataset_flags, field = "TADA.SampleFraction.Flag")
TADA::TADA_FieldValuesPie(dataset_flags, field = "TADA.MethodSpeciation.Flag")
TADA::TADA_FieldValuesPie(dataset_flags, field = "TADA.ResultUnit.Flag")
TADA::TADA_FieldValuesPie(dataset_flags, field = "TADA.ActivityType.Flag")
Any results flagged as “Invalid” are recognized in the QAQC Validation Table as having some data quality issue. “NonStandardized” means that the format has not been fully vetted or processed, while “Valid” confirms that the characteristic combination is widely recognized as correctly formatted. Let’s add any invalid results to the removed dataset for later review.
Note: if you find any errors in the QAQC Validation Table, please contact the WQX Help Desk at WQX@epa.gov to help correct it. Thanks in advance!
# grab all the flagged results from the four functions
problem_flagged <- dataset_flags %>%
filter(TADA.SampleFraction.Flag == "Invalid" | TADA.MethodSpeciation.Flag == "Invalid" | TADA.ResultUnit.Flag == "Invalid" | !TADA.ActivityType.Flag %in% ("Non_QC")) %>%
dplyr::mutate(TADA.RemovalReason = "Invalid Unit, Method, Speciation, or Activity Type.")
dataset_flags <- dataset_flags %>% dplyr::filter(!ResultIdentifier %in% problem_flagged$ResultIdentifier)
# create data frame of removed results
removed <- plyr::rbind.fill(removed, problem_flagged)
# remove df no longer needed
rm(problem_flagged)
# dimension check
dimCheck(all_result_num, dataset_flags, removed, checkName = "Filter Flag Functions")## [1] "Good to go. Zero results created or destroyed in Filter Flag Functions check."Question 7: Are there any other metadata columns that you review and filter in your workflow?
We’ve finished running the recommended flagging functions and removing results that do not pass QC checks. Let’s look at the breakdown of these data in the removed object.
removal <- TADA::TADA_FieldValuesTable(removed, field = "TADA.RemovalReason")
removal## Value
## 1 Activity media is not water.
## 2 Result value type cannot be converted to numeric or no detection limit values provided.
## 3 Invalid Unit, Method, Speciation, or Activity Type.
## 4 Detection limit information contains errors or missing information.
## Count
## 1 4679
## 2 3977
## 3 1776
## 4 105You can review any other columns of interest and create custom domain tables of your “Valid” and “Invalid” criteria using R or Excel. Also check out some of the other flagging functions available in TADA:
?TADA_FindNearbySites()- under active development?TADA_FindPotentialDuplicatesMultipleOrgs()?TADA_FindPotentialDuplicatesSingleOrg()?TADA_FindQAPPApproval()?TADA_FindQAPPDoc()?TADA_FlagAboveThreshold()?TADA_FlagBelowThreshold()?TADA_FlagContinuousData()?TADA_FlagCoordinates()?TADA_FlagMeasureQualifierCode()?TADA_FlagMethod()
Please let us know of other flagging functions you think would have broad appeal in the TADA package or need assistance brainstorming/developing.
Censored data handling
We have already identified, flagged, and in some cases removed problematic detection limit data from our dataset, but that doesn’t keep them from being difficult. Because we do not know the result value with adequate precision, water quality data users often set non-detect values to some number below the reported detection limit. TADA contains some simple methods for handling detection limits: users may multiply the detection limit by some number between 0 and 1, or convert the detection limit value to a random number between 0 and the detection limit. More complex detection limit estimation requiring regression models (Maximum Likelihood, Kaplan-Meier, Robust Regression on Order Statistics) or similar must be performed outside of the current version of TADA (though future development is planned).
Question 8: How would you parameterize
TADA_SimpleCensoredMethods() to make non-detect values
equal to the provided detection limit? What would you need to change in
the example below?
dataset_cens <- TADA::TADA_SimpleCensoredMethods(dataset_flags, nd_method = "multiplier", nd_multiplier = 0.5, od_method = "as-is")Let’s take a look at how the censored data handling function affects
the TADA.ResultMeasureValueDataTypes.Flag column.
First, we can look use TADA_FieldValuesTable to look at
the TADA.ResultMeasureValueDataTypes.Flag column in data set before we
ran TADA_SimpleCensoredMethods.
# before
TADA::TADA_FieldValuesTable(dataset_flags, field = "TADA.ResultMeasureValueDataTypes.Flag")## Value Count
## 1 Numeric 137517
## 2 Result Value/Unit Copied from Detection Limit 4031
## 3 Percentage 913
## 4 Numeric Range - Averaged 33
## 5 Less Than 19
## 6 Greater Than 3
## 7 Comma-Separated Numeric 1Then we can use TADA_FieldValuesTable again to look at
the same column after TADA_SimpleCensoredMethods.
# after
TADA::TADA_FieldValuesTable(dataset_cens, field = "TADA.ResultMeasureValueDataTypes.Flag")## Value Count
## 1 Numeric 137498
## 2 Result Value/Unit Estimated from Detection Limit 4045
## 3 Percentage 913
## 4 Numeric Range - Averaged 33
## 5 Less Than 19
## 6 Result Value/Unit Copied from Detection Limit 5
## 7 Greater Than 3
## 8 Comma-Separated Numeric 1Question 9: Is there a difference between the first and second tables?
If you’d like to start thinking about using statistical methods to
estimate detection limit values, check out the ?TADA_Stats
function, which accepts user-defined data groupings (or defaults to
TADA.ComparableDataIdentifier to determine measurement count, location
count, censored data stats, min, max, and percentile stats, and suggests
non-detect estimatiom methods based on the number of results, % of
dataset censored, and number of censoring levels (detection limits). The
decision tree used in the function was outlined in a National
Nonpoint Source Tech Memo.
Data exploration
How are you feeling about your test dataset? Does it seem ready for
the next step(s) in your analyses? There’s probably a lot more you’d
like to look at/filter out before you’re ready to say: QC complete.
Let’s first check out characteristics in the dataset using
dplyr functions and pipes.
# get table of characteristics with number of results, sites, and organizations
dataset_cens_summary <- dataset_cens %>%
dplyr::group_by(TADA.CharacteristicName) %>%
dplyr::summarise(Result_Count = length(ResultIdentifier), Site_Count = length(unique(MonitoringLocationIdentifier)), Org_Count = length(unique(OrganizationIdentifier))) %>%
dplyr::arrange(desc(Result_Count)) You may see a characteristic that you’d like to investigate further
in isolation. TADA_FieldValuesPie() will also produce
summary pie charts for a given column within a specific
characteristic. Let’s take a look.
# go ahead and pick a characteristic name from the table generated above. I picked dissolved oxygen (DO) amd selected OrganizationFormalName as the field to see the relative contribution of each org to DO results
TADA::TADA_FieldValuesPie(dataset_cens, field = "OrganizationFormalName", characteristicName = "DISSOLVED OXYGEN (DO)")
We can view the site locations using a TADA mapping function. In this map, the circles indicate monitoring locations in the dataset; their size corresponds to the number of results collected at that site, while the darker the circle, the more characteristics were sampled at that site.
TADA::TADA_OverviewMap(dataset_cens)Out of curiosity, let’s take a look at a breakdown of these monitoring location types. Do they all indicate surface water samples? Depending upon your program’s goals and methods, you might want to filter out some of the types you see.
TADA::TADA_FieldValuesPie(dataset_cens, field = "MonitoringLocationTypeName")
One of the next big steps is data harmonization: translating and
aggregating synonyms, combining multiple forms/species of certain
characteristics, etc. We won’t get to that in this demo (more details
can be found here: TADA
Module 1: Water Quality Portal Data Discovery and Cleaning or TADA_HarmonizeSynonyms()),
but for now we can start looking at data distributions within a single
characteristic-speciation-fraction-unit using the plotting functions
TADA_Histogram() and TADA_Boxplot(). We can
also view a stats table using TADA_Stats.
Let’s first take a look at the column TADA.ComparableDataIdentifier, which breaks down characteristics into groups by name, fraction, speciation, and unit. These four columns are important to evaluate together to ensure the plotted data are sufficiently similar to appear on a single plot together: it doesn’t make sense to plot characteristics with different units or fractions in the same distribution.
# trusty field values table - lets just look at the first few entries with the most associated records
compid <- TADA::TADA_FieldValuesTable(dataset_cens, field = "TADA.ComparableDataIdentifier")Now that we have an idea for what the TADA.ComparableDataIdentifier looks like, we can check out how it is used to plot distinct characteristic groups.
# Look at a histogram, boxplot, and stats for TADA.ComparableDataIdentifier(s) of your choice.
comp_data_id <- "PH_NA_NA_NONE"
plot_data <- subset(dataset_cens, dataset_cens$TADA.ComparableDataIdentifier %in% comp_data_id)Question 10: How does selecting the different options on the left side of the histogram change the data displayed? When might you want to use a histogram vs. a boxplot?
Let’s take a look at the histogram and boxplot for the comparable data identifier we selected.
TADA::TADA_Histogram(plot_data, id_cols = "TADA.ComparableDataIdentifier")## [1] "Plotting function removed 7 results where TADA.ResultMeasureValue = NA. These results cannot be plotted."
TADA::TADA_Boxplot(plot_data, id_cols = "TADA.ComparableDataIdentifier")## [1] "Plotting function removed 7 results where TADA.ResultMeasureValue = NA. These results cannot be plotted."
stats <- TADA::TADA_Stats(plot_data)## [1] "Dataset contains 7 results missing both a TADA result value and a detection limit. These values will not be represented in the stats summary table. Suggest removing or handling."We can also explore depth profiles for selected characteristics at
specific site on a single date. There are a few functions that can help
with this. First we can use TADA_FlagDepthCategory to place
results into various depth categories (surface, middle, and bottom).
#
dataset_depth <- TADA::TADA_FlagDepthCategory(dataset_cens)## [1] "TADA_FlagDepthCategory: checking data set for depth values. 70304 results have depth values available."
## [1] "TADA_FlagDepthCategory: assigning depth categories."
## [1] "TADA_FlagDepthCategory: Grouping results by MonitoringLocationIdentifier, OrganizationIdentifier, CharacteristicName, and ActivityStartDate for aggregation for entire water column."
## [1] "TADA_FlagDepthCategory: No aggregation performed."We can also use another function, TADA_IDDepthProfiles
to identify location/date/characteristic combinations in the data set
that can be used for depth profile plots or analysis. The default number
of values required to identify a location/date/characteristic as a depth
profile is 2, but this can be changed by the user. We will specify a
larger value, 5, so that any depth profiles identified will have results
from at least 5 different depths.
#
depth_profile_id <- TADA::TADA_IDDepthProfiles(dataset_depth, nvalue = 5)Question 11: How can TADA_IDDepthProfiles() help users use TADA_DepthProfilePlot most efficiently?
Now, we can use TADA_DepthProfilePlot to plot up to
three characteristics against depth. In this example, we will look at
pH, secchi depth, and pH.
#
TADA::TADA_DepthProfilePlot(dataset_cens,
groups = c('TEMPERATURE, WATER_NA_NA_DEG C', 'DEPTH, SECCHI DISK DEPTH_NA_NA_M', 'PH_NA_NA_NONE'),
location = "REDLAKE_WQX-ANKE",
activity_date = "2018-10-04",
depthcat = TRUE,
surfacevalue = 2,
bottomvalue = 2,
unit = "m")## [1] "TADA_DepthProfilePlot: Running TADA_DepthCategoryFlag function to add required columns to data frame"
## [1] "TADA_FlagDepthCategory: checking data set for depth values. 70304 results have depth values available."
## [1] "TADA_FlagDepthCategory: assigning depth categories."
## [1] "TADA_FlagDepthCategory: Grouping results by MonitoringLocationIdentifier, OrganizationIdentifier, CharacteristicName, and ActivityStartDate for aggregation for entire water column."
## [1] "TADA_FlagDepthCategory: No aggregation performed."
## [1] "TADA_DepthProfilePlot: Depth unit in data set matches depth unit specified by user for plot. No conversion necessary."
## [1] "TADA_DepthProfilePlot: Identifying available depth profile data."
## [1] "TADA_DepthProfilePlot: Any results for DEPTH, SECCHI DISK DEPTH, DEPTH, SECCHI DISK DEPTH (CHOICE LIST), DEPTH, SECCHI DISK DEPTH REAPPEARS, DEPTH, DATA-LOGGER (NON-PORTED), DEPTH, DATA-LOGGER (PORTED), RBP STREAM DEPTH - RIFFLE, RBP STREAM DEPTH - RUN, THALWEG DEPTH match the depth unit selected for the figure."
## [1] "TADA_DepthProfilePlot: Adding surface delination to figure."
## [1] "TADA_DepthProfilePlot: Adding bottom delination to figure."Finally, we can download our PASS and FAIL data sets together into an Excel spreadsheet.
dataset_and_removed <- dplyr::bind_rows(dataset_cens, removed)
# Un-comment to download Excel spreadsheet to your working directory
# install.packages(writexl)
# library(writexl)
# writexl::write_xlsx(dataset_and_removed, "NCTCShepherdstownData.xlsx")TADA R Shiny Modules
Finally, take a look at an alternative workflow for QC’ing WQP data: TADA Shiny Module 1: Data Discovery and Cleaning. This is a Shiny application that runs many of the TADA functions covered in this training document behind a graphical user interface. The shiny application queries the WQP, contains maps and data visualizations, flags suspect data results, handles censored data, and more. You can launch it using the code below.
# download TADA Shiny repository
remotes::install_github("USEPA/TADAShiny", ref = "develop", dependencies = TRUE)
# launch the app locally.
TADAShiny::run_app()DRAFT Module 1 is also currently hosted on the web with minimal server memory/storage allocated.